ICFS-Plus: Actuarial Software for the Property and Causality Insurance Industry

Videos marked with an (*) contain discussion of new content in ICRFS-Plus™ 12.


If for any reason you are unable to view the training or demonstration videos, please contact our support staff at support@insureware.com and we will arrange to send you a copy of the videos on CD-ROM. You will be able to run the videos from the CD.


The training videos should be used for hands on training. We suggest you run the videos on a separate computer using a data projector, and train as a group.

The only way you will learn all the new concepts and be able to exploit all the immense benefits is by using the system. Experiential learning is imperative.

It is important that you study the videos in sequential order as set out below.



Table of Contents

1. Introduction to ICRFS-Plus™ 12 and modelling modules

    2. Modelling using the Link Ratio Techniques and Extended Link Ratio Family modelling framework

      3. Introduction to the Probabilistic Trend Family modelling framework

        4. Modelling real data (CTP) in the PTF modelling framework

        5. TG CS5: heteroscedasticity and varying parameters

          6. TG ABC: modelling wizard, simulations, and release of capital as profit

            7. Importing of data from other applications and COM Automation

              8. Further PTF Modelling Examples

                9. Layers and the PALD Module

                  10. Introduction to MPTF

                    11. Clusters and MPTF Concepts

                      12. Capital Management of long tail liabilities

                        13. Solvency II one year risk horizon: SCR, Best Estimate of Liabilities (BEL), Technical Provisions (TP), and Market Value (Risk) Margins (MVM) for the aggregate of long-tail LOBs

                          14. Other applications of the MPTF modelling framework

                            15. The Bootstrap: how it shows the Mack method doesn't work

                              16. Updates from 10.6 to 11

                                4. Modelling real data (CTP) in the PTF modelling framework

                                4.1 Modelling CTP in PTF manually


                                In this video, we model the triangle group CTP using the PTF modelling framework. The method of evaluating whether parameters are needed and the method of adding parameters is illustrated. In general, we model development periods first, then whichever direction (accident or calendar) shows the most changes (from left to right).


                                The methods of modelling: adding trends, detecting outliers, maintaining the normality assumption, and optimisation are covered.


                                The statistically optimal model has the lowest (Bayes Information Criterion) BIC.


                                The process of validation is discussed. If the trends in the data are stable, then we expect to find that the model does a well for predicting the most recent years if they are removed from the model estimation. This is shown to be the case for this data.



                                4.2 Modelling CTP using the Wizard and ELRF


                                In this video we study the wizard and how it models the triangle group CTP (dataset PL(I)). The wizard is simply a set of commands - similar to a macro. As the wizard builds a model, it evaluates the model structure and, depending on the structure identified, it will consider different alternative models.


                                In general, the wizard does a good job for modelling data. However, it is always necessary to evaluate a wizard model. That is, you need to determine which wizard model is best, to view outliers, and to check any trends set to zero. If you are not happy with any suggested wizard model, it is always possible to either adjust the wizard model or to build a model manually.


                                The functionality of PTF as available on the toolbar is also revised.


                                CTP is briefly examined in the ELRF modelling framework to determine whether ratios have any predictive power.